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Search Results (2,390)

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33 pages, 2283 KiB  
Article
High-Performance Structures of Biopolymer Gels Activated with Scleroprotein Crosslinkers
by Miroslawa Prochon, Oleksandra Dzeikala and Szymon Szczepanik
Molecules 2025, 30(3), 627; https://doi.org/10.3390/molecules30030627 - 31 Jan 2025
Viewed by 229
Abstract
The study explores innovative crosslinking processes for biopolymer gel materials using amino acids and ion-redox initiators to significantly enhance their structural and functional properties. Advanced analytical techniques, including FTIR, Raman spectroscopy, XRD, TEM, TGA, DSC, ToF-SIMS, SEM/EDS, GPC/SEC, and elemental analysis, were employed [...] Read more.
The study explores innovative crosslinking processes for biopolymer gel materials using amino acids and ion-redox initiators to significantly enhance their structural and functional properties. Advanced analytical techniques, including FTIR, Raman spectroscopy, XRD, TEM, TGA, DSC, ToF-SIMS, SEM/EDS, GPC/SEC, and elemental analysis, were employed for comprehensive material characterization. The synthesized materials show potential applications in packaging and medicine, particularly for single-use products with short life cycles. Two crosslinking strategies were developed. The first combines gelatin with polyvinyl alcohol (PVA); keratin hydrolysate; and amino acids such as cysteine, hydroxyproline, proline, and histidine. The second employs endogenous cysteine, activated by ion-redox initiators, leveraging its trans-sulfuration ability to form highly stable polymer networks with optimized mechanical and thermal properties. Notably, the synergy between cysteine and potassium persulfate redox initiators proved particularly effective, making this approach attractive for industrial applications. This study introduces novel crosslinking methods and highlights the potential of amino acid-based strategies for designing advanced biopolymer gels with enhanced properties. Full article
(This article belongs to the Special Issue Bio-Based Polymers for Sustainable Future)
25 pages, 2361 KiB  
Article
How Does Rural Resilience Affect Return Migration: Evidence from Frontier Regions in China
by Yiqing Su, Meiqi Hu and Xiaoyin Zhang
Systems 2025, 13(2), 89; https://doi.org/10.3390/systems13020089 - 31 Jan 2025
Viewed by 279
Abstract
An important way to realize urban–rural integration and regional coordinated development is to attract labor forces back to rural areas. Most of the existing studies consider the impact of individual factors on population migration, they lack a systematic framework to analyze the combined [...] Read more.
An important way to realize urban–rural integration and regional coordinated development is to attract labor forces back to rural areas. Most of the existing studies consider the impact of individual factors on population migration, they lack a systematic framework to analyze the combined impact of different factors on rural return migration. Furthermore, in practice, the interaction within the rural social ecosystem as an important driver of return migration is always ignored. Using data from 131 villages in 14 cities in Guangxi, China, combined with the Coupled Infrastructure System framework and the sustainable livelihoods framework, this paper analyzes the comprehensive impact of internal components of the rural social ecosystem on return migration. Qualitative comparative analysis is used to identify four condition combinations that can effectively promote return migration and five condition combinations that make return migration vulnerable. The main conclusions are as follows. First, high-level public infrastructure providers are an important driving factor for labor return to rural areas, and a substitution effect exists between them and livelihood capitals. Second, sufficient human capital and social capital are crucial for return migration, highlighting the importance of the structure of rural members and the collective atmosphere. Third, natural capital and economic capital emphasized by previous research are not key conditions for forming a high level of return migration. Fourth, the vulnerability of return migration is mainly caused by the decline of social capital, the loss of public infrastructure providers, and excessive dependence on economic or physical capital input. To attract return migration, rural areas need to pay attention to the integration and synergy of multi-dimensional capital and public infrastructure providers, and special emphasis should be placed on the cultivation of public leadership to promote the enhancement of human capital and social capital. This paper provides a more comprehensive and instrumental analytical perspective for understanding and promoting rural return migration. While deepening the understanding of the dynamic relationship between rural social ecosystem and labor mobility, it also offers policy insights for developing countries to achieve integrated urban–rural development. Full article
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<p>Coupled Infrastructure System (CIS) framework (Source: Anderies and Janssen [<a href="#B39-systems-13-00089" class="html-bibr">39</a>]).</p>
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<p>Theoretical framework diagram.</p>
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<p>Research zoning map.</p>
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14 pages, 358 KiB  
Article
The Influence of Green Finance on “Dual Carbon” Goals: Analyzing the Functions of Government and Market
by Meisha Zhang, Yongfang Wu and Hang Su
Sustainability 2025, 17(3), 1122; https://doi.org/10.3390/su17031122 - 30 Jan 2025
Viewed by 282
Abstract
Building an ecologically sustainable civilization and promoting green development not only make up the new motive power for China’s economic growth but are also an inevitable choice for achieving the “Dual Carbon” goal. This paper draws on the results of China’s provincial panels [...] Read more.
Building an ecologically sustainable civilization and promoting green development not only make up the new motive power for China’s economic growth but are also an inevitable choice for achieving the “Dual Carbon” goal. This paper draws on the results of China’s provincial panels from 2012 to 2021 and constructs a thorough assessment index system for green finance that includes five dimensions: standardized system, disclosure of information, policy incentives, products and market, international cooperation. The influence mechanism of green finance on the realization of the “Dual Carbon” goal is revealed based on both quantity and caliber perspectives of green technological innovation, and the governments’ and markets’ regulating roles are analyzed. The study’s findings imply that (1) green finance facilitates the achievement of the “Dual Carbon” goal; (2) green finance helps to achieve the “Dual Carbon” goal by boosting green technology innovation and, compared with strategic green innovation, the effect of substantive green innovation is more significant; and (3) government support and increased marketization can bolster green finance’s contribution to accomplishing the goal. This study not only theoretically breaks through the limitations of the existing green finance evaluation index but also expands the single “quantity” channel of the impact of green finance on carbon emissions to a more comprehensive “quantity” and “caliber” channel, and also provides countermeasures and guidelines for how to better play the “synergy” of the government and the market in the practice of green finance. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
23 pages, 6653 KiB  
Article
Monitoring Welfare of Individual Broiler Chickens Using Ultra-Wideband and Inertial Measurement Unit Wearables
by Imad Khan, Daniel Peralta, Jaron Fontaine, Patricia Soster de Carvalho, Ana Martos Martinez-Caja, Gunther Antonissen, Frank Tuyttens and Eli De Poorter
Sensors 2025, 25(3), 811; https://doi.org/10.3390/s25030811 - 29 Jan 2025
Viewed by 418
Abstract
Monitoring animal welfare on farms and in research settings is attracting increasing interest, both for ethical reasons and for improving productivity through the early detection of stress or diseases. In contrast to video-based monitoring, which requires good light conditions and has difficulty tracking [...] Read more.
Monitoring animal welfare on farms and in research settings is attracting increasing interest, both for ethical reasons and for improving productivity through the early detection of stress or diseases. In contrast to video-based monitoring, which requires good light conditions and has difficulty tracking specific animals, recent advances in the miniaturization of wearable devices allow for the collection of acceleration and location data to track individual animal behavior. However, for broilers, there are several challenges to address when using wearables, such as coping with (i) the large numbers of chickens in commercial farms,(ii)the impact of their rapid growth, and (iii) the small weights that the devices must have to be carried by the chickens without any impact on their health or behavior. To this end, this paper describes a pilot study in which chickens were fitted with devices containing an Inertial Measurement Unit (IMU) and an Ultra-Wideband (UWB) sensor. To establish guidelines for practitioners who want to monitor broiler welfare and activity at different scales, we first compare the attachment methods of the wearables to the broiler chickens, taking into account their effectiveness (in terms of retention time) and their impact on the broiler’s welfare. Then, we establish the technical requirements to carry out such a study, and the challenges that may arise. This analysis involves aspects such as noise estimation, synergy between UWB and IMU, and the measurement of activity levels based on the monitoring of chicken activity. We show that IMU data can be used for detecting activity level differences between individual animals and environmental conditions. UWB data can be used to monitor the positions and movement patterns of up to 200 animals simultaneously with an accuracy of less than 20 cm. We also show that the accuracy depends on installation aspects and that errors are larger at the borders of the monitored area. Attachment with sutures had the longest mean retention of 19.5 days, whereas eyelash glue had the shortest mean retention of 3 days. To conclude the paper, we identify current challenges and future research lines in the field. Full article
(This article belongs to the Special Issue Flexible and Wearable Sensors and Sensing for Agriculture and Food)
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<p>Tag containing UWB, IMU, and battery.</p>
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<p>Attachment methods tested in this study. (<b>a</b>) Sutures. (<b>b</b>) Adhesives. (<b>c</b>) Elastic straps. (<b>d</b>) Three-dimensional printed backpack. (<b>e</b>) Harness.</p>
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<p>Mean retention time for each retention method. Dots indicate individual measurements.</p>
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<p>Frequency of measurement at each location in the four pens of the noise measurement experiment. Each colored blob corresponds to one UWB tag that was left on the ground for 24 h; the area size indicates the magnitude of the noise. The units of measurement on both the <span class="html-italic">x</span>-axis and <span class="html-italic">y</span>-axis are in centimeters.</p>
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<p>Blue points mark the average locations measured for the tags; the red ellipses mark their 95% confidence interval (assuming Gaussian distribution).</p>
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<p>Heatmaps of chicken locations in the pen. The left plot aggregates data from all chickens; on the right, a separate heatmap is depicted for each individual chicken. Red areas indicate frequent occupation.</p>
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<p>Estimated distance walked every 4 h by each chicken. The data are from the second round of the experiment.</p>
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<p>Distribution of accelerations measured in the three axes for each chicken.</p>
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<p>Distribution and cumulated probability (zoomed) of the acceleration magnitude for each chicken before normalization.</p>
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<p>Cumulative distribution of the difference between the acceleration magnitude and its mean.</p>
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<p>Proportion of active time in 4 h intervals for each chicken.</p>
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<p>Histogram of activity measures at each location of the pen for each chicken. Red indicates higher values.</p>
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<p>Correlation movement vs. IMU.</p>
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<p>Histogram IMU vs. movements.</p>
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16 pages, 1374 KiB  
Article
Central (Hemodynamic) and Peripheral (Autonomic) Synergy During Persuasion Within a Shared Decision-Making Process
by Laura Angioletti, Carlotta Acconito, Federica Saquella and Michela Balconi
Appl. Sci. 2025, 15(3), 1361; https://doi.org/10.3390/app15031361 - 28 Jan 2025
Viewed by 416
Abstract
This hyperscanning study explores the central (hemodynamic) and peripheral (autonomic) markers of persuasion within a shared decision-making process. Decision-making was examined through a task where two decision-makers assumed the role of Persuader (P-der) and Persuaded (P-ded), with the P-der aiming to increase group [...] Read more.
This hyperscanning study explores the central (hemodynamic) and peripheral (autonomic) markers of persuasion within a shared decision-making process. Decision-making was examined through a task where two decision-makers assumed the role of Persuader (P-der) and Persuaded (P-ded), with the P-der aiming to increase group decision orientation in the P-ded. Data were collected from 14 dyads using functional near-infrared spectroscopy to measure prefrontal cortex (PFC) hemodynamic activity and collection and recording of autonomic indices including heart rate (HR) and HR variability (HRV). The analysis focused on two phases: Phase 1, where the P-der presented the scenario and enacted their persuasive strategy, and Phase 2, characterized by the P-ded’s response. The results revealed significant effects on the dissimilarity indices at the dyadic level. Compared with Phase 1, Phase 2 included higher oxygenated hemoglobin dissimilarity in the PFC, indicating greater inter-dyadic divergence during the P-ded’s response. HR dissimilarity increased when the P-ded spoke, suggesting disrupted synergy, while HRV dissimilarity was higher when the P-der spoke, potentially reflecting differences in stress regulation. These findings suggest that neurophysiological coherence varies based on persuasion phases within shared decision-making, with P-ded introducing greater dissonance in dyads synergy. Compared with single-subject approaches, dyadic analyses offer a more accurate understanding of the interpersonal nature of persuasion dynamics during decision-making. Full article
(This article belongs to the Section Applied Neuroscience and Neural Engineering)
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<p>Description of the experimental procedure.</p>
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<p>The fNIRS setup included four light sources (marked in yellow) placed at AF3, AF4, F5, and F6, and four detectors (marked in orange) positioned at AFF1h, AFF2h, F3, and F4. Six measurement channels (shown in violet) were established as follows: Ch1 (AF3-F3), Ch2 (AF3-AFF1h), Ch3 (F5-F3), Ch4 (AF4-F4), Ch5 (AF4-AFF2h), and Ch6 (F6-F4).</p>
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<p>Dyads’ hemodynamic results. The bar graph displays the main effect of Phase, for which higher dissimilarity (Euclidean distance) was found at the inter-brain level in Phase 2 compared with Phase 1. Bars represent ±1 standard error and stars (*) mark statistically significant effects.</p>
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<p>(<b>A</b>,<b>B</b>) Dyads’ autonomic results. (<b>A</b>) The bar graph displays the main effect of Speaking Condition, for which an increase in the dissimilarity index for the HR was found when the speaker was the P-ded compared with when the speaker was the P-der. (<b>B</b>) The bar chart displays the main effect of Speaking Condition, for which an increase in the dissimilarity index for HRV was detected when the speaker was the P-der compared with when the speaker was the P-ded. Bars represent ±1 standard error and stars (*) mark statistically significant differences.</p>
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18 pages, 6241 KiB  
Article
Optimizing Quercetin Extraction from Taraxacum mongolicum Using Ionic Liquid–Enzyme Systems and Network Pharmacology Analysis
by Jingwei Hao, Yifan Sun, Nan Dong, Yingying Pei, Xiangkun Zhou, Yi Zhou and Heming Liu
Separations 2025, 12(2), 34; https://doi.org/10.3390/separations12020034 - 28 Jan 2025
Viewed by 392
Abstract
Quercetin in Taraxacum mongolicum was extracted by ultrasound-assisted extraction in synergy with an ionic liquid–enzyme complex system, and the antioxidant function of quercetin was investigated based on network pharmacology. From 1-butyl-3-methylimidazolium chloride, 1-butyl-3-methylimidazolium acetate, 1-butyl-3-methylimidazolium tetrafluoroborate, 1-butyl-3-methylimidazolium bromide, and 1-butyl-3-methylimidazolium tetrafluoroborate, the first [...] Read more.
Quercetin in Taraxacum mongolicum was extracted by ultrasound-assisted extraction in synergy with an ionic liquid–enzyme complex system, and the antioxidant function of quercetin was investigated based on network pharmacology. From 1-butyl-3-methylimidazolium chloride, 1-butyl-3-methylimidazolium acetate, 1-butyl-3-methylimidazolium tetrafluoroborate, 1-butyl-3-methylimidazolium bromide, and 1-butyl-3-methylimidazolium tetrafluoroborate, the first step was to choose the appropriate ionic liquid. Subsequently, a response surface methodology and single-factor experiment were used to optimize the extraction process. The quercetin and the key targets for antioxidants were obtained from a public database. Antioxidant activity was assessed by measuring the scavenging rate of 1,1-diphenyl-2-picrylhydrazyl (DPPH) radicals and hydroxyl radicals(•OH). The approach revealed that the optimal extraction process was the liquid–solid ratio of 31.62:1 mL/g, enzymatic temperature of 55 °C, and the amount of cellulase added was 14.79% of the dry weight of dandelion. Under this condition, the yield of quercetin was 0.24 ± 0.011 mg/g, which was 1.3 times higher than that of the conventional reflux extraction method of 0.185 ± 0.015 mg/g. Pharmacological findings showed 57 cross-targets of quercetin with antioxidants. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that antioxidant function may be related to chemical carcinogenesis-reactive oxygen species, and the Phosphoinositide 3-kinase/protein kinase B signaling pathway. Quercetin has strong DPPH and •OH radical scavenging activity. The development and use of industrial dandelion are supported by this sustainable and effective method of extracting quercetin from dandelion. Full article
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<p>The effect of different parameters on the extraction yield of quercetin from dandelion: (<b>a</b>) ionic liquid; (<b>b</b>) the concentration of [BMIM]Br; (<b>c</b>) liquid–solid ratio; (<b>d</b>) ultrasonic temperature; (<b>e</b>) enzyme addition; (<b>f</b>) enzymatic hydrolysis temperature. The data are shown as mean ± SD (<span class="html-italic">n</span> = 3). Different letters have significant differences in the mean at the 0.05 level.</p>
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<p>The effect of different parameters on the extraction yield of quercetin from dandelion: (<b>a</b>) ionic liquid; (<b>b</b>) the concentration of [BMIM]Br; (<b>c</b>) liquid–solid ratio; (<b>d</b>) ultrasonic temperature; (<b>e</b>) enzyme addition; (<b>f</b>) enzymatic hydrolysis temperature. The data are shown as mean ± SD (<span class="html-italic">n</span> = 3). Different letters have significant differences in the mean at the 0.05 level.</p>
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<p>Contour plots and response surface plots of dandelion extracts affected by enzymatic hydrolysis temperature (A), enzyme addition (B), and liquid–solid ratio (C) on quercetin yield.</p>
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<p>Network pharmacology analysis of quercetin–antioxidant relationship: (<b>a</b>) Venn diagram of quercetin–antioxidant targets; (<b>b</b>) quercetin–antioxidant target network. Green rectangular nodes represent dandelions; blue hexagonal nodes represent quercetin; orange octagonal nodes represent antioxidant functions; and purple oval nodes represent intersecting targets.</p>
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<p>Protein–protein interaction (PPI) network.</p>
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<p>GO and KEGG analysis of the targets: (<b>a</b>) GO function enrichment of quercetin–antioxidant targets; (<b>b</b>) KEGG enrichment of quercetin–antioxidant targets. GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes. BP: Biological Process; CC: Cellular Component; MF: Molecular Function.</p>
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<p>The antioxidant ability of the quercetin: (<b>a</b>) DPPH radical scavenging activity; (<b>b</b>) •OH radical scavenging activity. Data are shown as the mean ± SD (<span class="html-italic">n</span> = 3).</p>
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24 pages, 426 KiB  
Review
Alzheimer’s Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery
by Jose Dominguez-Gortaire, Alejandra Ruiz, Ana Belen Porto-Pazos, Santiago Rodriguez-Yanez and Francisco Cedron
Int. J. Mol. Sci. 2025, 26(3), 1004; https://doi.org/10.3390/ijms26031004 - 24 Jan 2025
Viewed by 423
Abstract
Alzheimer’s disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements have shifted the focus from the traditionally dominant amyloid hypothesis toward a multifactorial understanding of the disease. Emerging evidence suggests that while amyloid-beta (Aβ [...] Read more.
Alzheimer’s disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements have shifted the focus from the traditionally dominant amyloid hypothesis toward a multifactorial understanding of the disease. Emerging evidence suggests that while amyloid-beta (Aβ) accumulation is central to AD, it may not be the primary driver but rather part of a broader pathogenic process. Novel hypotheses have been proposed, including the role of tau protein abnormalities, mitochondrial dysfunction, and chronic neuroinflammation. Additionally, the gut–brain axis and epigenetic modifications have gained attention as potential contributors to AD progression. The limitations of existing therapies underscore the need for innovative strategies. This study explores the integration of machine learning (ML) in drug discovery to accelerate the identification of novel targets and drug candidates. ML offers the ability to navigate AD’s complexity, enabling rapid analysis of extensive datasets and optimizing clinical trial design. The synergy between these themes presents a promising future for more effective AD treatments. Full article
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<p>In this figure, the bold lines illustrate direct relationships or causal connections between the various pathogenic factors in Alzheimer’s disease, while the dotted lines denote feedback loops and amplification cycles that further exacerbate existing pathological processes. Overall, this schematic integrates multiple interdependent mechanisms, highlighting how genetic, environmental, and lifestyle factors converge to initiate mitochondrial dysfunction and oxidative stress, ultimately promoting amyloid-beta and tau protein aggregation. These misfolded proteins trigger synaptic dysfunction and neuronal death while also activating microglia and astrocytes that mount an inflammatory response, which, if sustained, amplifies protein aggregation and accelerates neurodegeneration. Moreover, vascular abnormalities and gut microbiome alterations potentiate both inflammation and oxidative damage, and imbalances in metal ions further promote pathological protein aggregation. Impaired autophagy and epigenetic modifications compound disease progression by hindering the clearance of toxic species and altering gene expression, respectively. By illustrating these interconnected pathways and feedback loops, the figure underscores the multifactorial nature of Alzheimer’s disease and the potential benefit of targeting multiple nodes in this network for therapeutic intervention.</p>
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20 pages, 2544 KiB  
Article
Glycosylated Delphinidins Decrease Chemoresistance to Temozolomide by Regulating NF-κB/MGMT Signaling in Glioblastoma
by Diego Carrillo-Beltrán, Yessica Nahuelpan, Constanza Cuevas, Karen Fabres, Pamela Silva, Jimena Zubieta, Giovanna Navarro, Juan P. Muñoz, María A. Gleisner, Flavio Salazar-Onfray, Noemi Garcia-Romero, Angel Ayuso-Sacido, Rody San Martin and Claudia Quezada-Monrás
Cells 2025, 14(3), 179; https://doi.org/10.3390/cells14030179 - 24 Jan 2025
Viewed by 462
Abstract
Glioblastoma (GB) is a highly malignant brain tumor with a poor prognosis, with a median survival of only 14.6 months despite aggressive treatments. Resistance to chemotherapy, particularly temozolomide (TMZ), is a significant challenge. The DNA repair enzyme MGMT and glioblastoma stem cells (GSCs) [...] Read more.
Glioblastoma (GB) is a highly malignant brain tumor with a poor prognosis, with a median survival of only 14.6 months despite aggressive treatments. Resistance to chemotherapy, particularly temozolomide (TMZ), is a significant challenge. The DNA repair enzyme MGMT and glioblastoma stem cells (GSCs) often mediate this resistance. Recent studies highlight the therapeutic potential of natural compounds, particularly delphinidins, found in deep purple berries. Delphinidins are known for their ability to inhibit NF-κB signaling, a critical pathway for GB progression, chemoresistance, and MGMT expression. Our research demonstrates that glycosylated delphinidins have potential adjuvant use in the treatment of GB, offering a promising natural strategy to combat TMZ resistance. Specifically, we observed that delphinidin 3,5 di-glucoside has potent anticancer effects when used alone. Meanwhile, delphinidin 3 glucoside acted in synergy with temozolomide to decrease cell viability, highlighting its potential as an adjuvant. It also exerted a faster and more sustained inhibition of NF-κB, highlighting its potential for long-lasting therapeutic effects. These findings open new avenues for targeted therapies against glioblastoma, particularly to overcome treatment resistance. Full article
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<p>Cytotoxicity assay of glycosylated delphinidins exposed to different concentrations in tumor and non-tumor glial cells. MTS assay to evaluate the viability of cells when exposed for 72 h to concentrations of 0, 15, 30, 60, 80, 100, 120, 180, and 240 μM of delphinidin 3 glucoside or delphinidin 3,5 di-glucoside. (<b>a</b>) U87-MG exposed to delphinidin 3 glucoside. (<b>b</b>) SVG-p12 exposed to delphinidin 3 glucoside. (<b>c</b>) GBM38 exposed to delphinidin 3 glucoside. (<b>d</b>) U87-MG exposed to delphinidin 3,5 di-glucoside. (<b>e</b>) SVG-p12 exposed to delphinidin 3,5 di-glucoside. (<b>f</b>) GBM38 exposed to delphinidin 3,5 di-glucoside. Data are presented as the mean ± standard deviation (SD); * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01 and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Glycosylated delphinidins reduce NF-κB activity in glioblastoma cells. (<b>a</b>) Luciferase activity normalized to GFP fluorescence intensity in U87-MG cells transfected with the pHAGE/NF-κB reporter vector treated with delphinidin-3-glucoside at concentrations of 15, 60, and 120 μM or with controls Bay117082 10 nM or LY294002 10 nM for 3 h. (<b>b</b>) 6 h. (<b>c</b>) 24 h. (<b>d</b>) 48 h. (<b>e</b>) Luciferase activity normalized to GFP fluorescence intensity in U87-MG cells transfected with the pHAGE/NF-κB reporter vector treated with delphinidin-3,5-di-glucoside at concentrations of 15, 60, and 120 μM or with the controls Bay117082 10 nM or LY294002 10 nM for 3 h. (<b>f</b>) 6 h. (<b>g</b>) 24 h. (<b>h</b>) 48 h. Data are presented as the mean ± standard deviation (SD); * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001 and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Glycosylate delphinidins reduce the levels of NF-κB pathway proteins that positively correlate with MGMT expression in glioblastoma in vitro. Protein array of the NF-κB signaling pathway performed in U87-MG cells treated with delphinidin 3 glucoside or delphinidin 3,5 di-glucoside at 120 µM for 24 h; the graph demonstrates the fold change compared to the control with DMSO. (<b>a</b>) U87-MG cells treated with delphinidin 3 glucoside. (<b>b</b>) U87-MG cells treated with delphinidin 3,5 di-glucoside. (<b>c</b>) Survival analysis with Kaplan–Meier plot of 220 glioblastoma cases; the red line represents tumors with high levels of SHARPIN, and the blue line represents tumors with low levels of the marker; the graph reports median survival and Hazard Ratio (HR) along with statistical significance. (<b>d</b>) Pearson correlation between SHARPIN and MGMT expression levels. (<b>e</b>) Survival analysis with Kaplan–Meier plot of 220 glioblastoma cases; the red line represents tumors with high levels of TMEM173, and the blue line represents tumors with low levels of the marker; the graph reports median survival and Hazard Ratio (HR) along with statistical significance. (<b>f</b>) Pearson correlation between TMEM173 and MGMT expression levels. Data are presented as the mean ± standard deviation (SD).</p>
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<p>Glycosylate delphinidins reduce the levels of NF-κB pathway proteins that positively correlate with MGMT expression in glioblastoma in vitro. Protein array of the NF-κB signaling pathway performed in U87-MG cells treated with delphinidin 3 glucoside or delphinidin 3,5 di-glucoside at 120 µM for 24 h; the graph demonstrates the fold change compared to the control with DMSO. (<b>a</b>) U87-MG cells treated with delphinidin 3 glucoside. (<b>b</b>) U87-MG cells treated with delphinidin 3,5 di-glucoside. (<b>c</b>) Survival analysis with Kaplan–Meier plot of 220 glioblastoma cases; the red line represents tumors with high levels of SHARPIN, and the blue line represents tumors with low levels of the marker; the graph reports median survival and Hazard Ratio (HR) along with statistical significance. (<b>d</b>) Pearson correlation between SHARPIN and MGMT expression levels. (<b>e</b>) Survival analysis with Kaplan–Meier plot of 220 glioblastoma cases; the red line represents tumors with high levels of TMEM173, and the blue line represents tumors with low levels of the marker; the graph reports median survival and Hazard Ratio (HR) along with statistical significance. (<b>f</b>) Pearson correlation between TMEM173 and MGMT expression levels. Data are presented as the mean ± standard deviation (SD).</p>
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<p>Glycosylate delphinidins reduce transcript and protein levels of MGMT in glioblastoma cells. RTqPCR was performed to evaluate MGMT transcript levels in U87-MG cells previously exposed to delphinidin 3 glucoside or delphinidin 3,5 di-glucoside at concentrations of 15, 60, and 120 μM for 24 or 48 h, BAY11-7082 was used at 10 μM. (<b>a</b>) RTqPCR of treatment delphinidin 3-glucoside for 24 h. (<b>b</b>) RTqPCR of treatment delphinidin 3-glucoside for 48 h. (<b>c</b>) RTqPCR of treatment delphinidin 3,5 di-glucoside for 24 h. (<b>d</b>) RTqPCR of treatment delphinidin 3,5 di-glucoside for 48 h. (<b>e</b>) WB was performed to assess MGMT protein levels in U87-MG cells when exposed to delphinidin 3-glucoside or delphinidin 3,5 di-glucoside for 24 h; the control BAY 11-7082 was used at 10 μM. (<b>f</b>) WB was performed to assess MGMT protein levels in U87-MG cells when exposed to delphinidin 3 glucoside or delphinidin 3,5 di-glucoside for 48 h. β-actin transcript was used as an endogenous control in the RTqPCR. Data are presented as mean ± standard deviation (SD); * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001 and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Glycosylated delphinidins negatively regulate MGMT promoter activity in glioblastoma cells. (<b>a</b>) The luciferase reporter assay was performed with the pmir-GlO MGMT promoter vector. To perform the assay, the U87-MG cells were previously transfected with the vector for 24 h; then treatments were carried out with delphinidin 3-glucoside at concentrations of 15, 60, and 120 μM for 24 h; BAY11-7082 was used as a positive regulation control. The data were normalized with the activity of renilla luciferase (<b>b</b>). The previous assay was performed with exposure to delphinidins for 48 h. (<b>c</b>) The activity of the MGMT promoter was measured when U87-MG cells were exposed to delphinidin 3,5 di-glucoside at concentrations of 15, 60, and 120 μM for 24 h. (<b>d</b>) Assay to measure MGMT promoter activity when cells are exposed for 48 h to delphinidin 3,5 di-glucoside. (<b>e</b>) Chromatin immunoprecipitation assay with anti p65/Rel-A antibody in U87.MG cells were exposed for 24 h to 120 μM of glycosylated delphinidins; the immuno-precipitate obtained with anti-RNA Polymerase II amplified with primers from the GAPDH promoter region was used as a normalizer. Data are presented as the mean ± standard deviation (SD); * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Sensitizing capacity of glycosylated delphinidins to the drug TMZ in glioblastoma cells. (<b>a</b>) MTS assay to assess the viability of U87-MG cells when exposed for 48 h to the combined treatments of TMZ at 100, 200, 400 and 800 μM, with delphinidin 3 glucoside doses at 15, 60 and 120 μM. BAY11-7082 20 μM was used as a positive sensitization control and DMSO as a vehicle control. Data were adjusted to percentages taking DMSO treatment as 100%. (<b>b</b>) Heat map of the previous MTS assessing delphinidin 3-glucoside-induced TMZ sensitization, including statistical analysis in each quadrant. (<b>c</b>) MTS assay to assess the viability of U87-MG cells when exposed for 48 h to the combined treatments of TMZ at 100, 200, 400, and 800 μM, with delphinidin 3,5 di-glucoside doses at 15, 60, and 120 µM. BAY11-7082 20 μM was used as a positive sensitization control, and DMSO was used as a vehicle control. Data were adjusted to percentages taking DMSO treatment as 100%. (<b>d</b>) Heatmap of the above MTS assessing the sensitization to TMZ induced by delphinidin 3,5 di-glucoside, including statistical analysis in each quadrant. (<b>e</b>) Summary table of percentages of apoptosis data obtained with the 48-h combinatorial treatment with 200 μM TMZ, 120 μM delphinidin 3 glucoside and 120 μM delphinidin 3,5 di-glucoside. 20 μM BAY11-7082 was used as a positive sensitization control and DMSO as a vehicle control. (<b>f</b>) Heat map of the previous apoptosis assay. Data are presented as the mean ± standard deviation (SD); * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>A proposed model of capacity of glycosylated delphinidins to decrease chemoresistance to Temozolomide by regulating NF-κB/MGMT (Created in <a href="https://BioRender.com" target="_blank">https://BioRender.com</a> (accessed on 12 December 2024)).</p>
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19 pages, 2793 KiB  
Article
Differential Effects of Pandemic-Related Stressors on Mental Health by Age and Sex
by Joshua B. Borja and Scott B. Patten
Healthcare 2025, 13(3), 224; https://doi.org/10.3390/healthcare13030224 - 23 Jan 2025
Viewed by 563
Abstract
Objective: There have been consistent concerns about a greater impact of COVID-19 on the mental health of younger people and females. We aimed to explore the potential synergistic effect of various pandemic-related stressors with age and sex on the mental health of the [...] Read more.
Objective: There have been consistent concerns about a greater impact of COVID-19 on the mental health of younger people and females. We aimed to explore the potential synergistic effect of various pandemic-related stressors with age and sex on the mental health of the general Canadian household population during the COVID-19 pandemic. Methods: Using cross-sectional data from the Statistics Canada 2022 Mental Health and Access to Care Survey (MHACS), frequencies for major depressive episode (MDE), generalized anxiety disorder (GAD), general psychological distress, and various pandemic-related and demographic factors were estimated. Odds ratios were estimated using binary logistic regression models. These estimates used a replicate bootstrapping procedure recommended by Statistics Canada. Finally, Relative Excess Risk due to Interaction (RERI) models were used for each outcome to evaluate the interactions of each pandemic-related stressor with age and sex on an additive scale. Results: Past-12-month MDE and GAD, psychological distress, and the various COVID-19 stressors were more prevalent in young people and females. Overall, the stressors were confirmed to be associated with these outcomes. There were greater-than-additive interactions between age and difficulty accessing healthcare, loneliness, physical health problems, household relationship challenges, and work stress; and between sex and severe illness of a loved one, loneliness, work stress, LGBTQ2+ status, marital status, difficulty accessing healthcare, physical health problems, job/income loss, and financial difficulties. Generally, evidence of synergy was found between age and pandemic-related stressors and sex and pandemic-related stressors. Conclusions: Greater-than-additive interactions of pandemic-related stressors with age and sex may indicate synergistic vulnerabilities within females and young people. Future pandemics, via corresponding stressors, may be associated with increased mental health vulnerability in females, youth, and especially young females. Full article
(This article belongs to the Special Issue Sexuality, Health, and Gender)
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<p>Forest plots summarizing the RERIs between age and various pandemic-related stressors for (<b>a</b>) MDE, (<b>b</b>) GAD, and (<b>c</b>) K10 distress. Red squares represent the point estimates for the RERIs between age and pandemic-related stressors; horizontal bars represent 95% CIs.</p>
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<p>Forest plots summarizing the RERIs between age and various pandemic-related stressors for (<b>a</b>) MDE, (<b>b</b>) GAD, and (<b>c</b>) K10 distress. Red squares represent the point estimates for the RERIs between age and pandemic-related stressors; horizontal bars represent 95% CIs.</p>
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<p>Forest plots summarizing the RERIs between sex and various pandemic-related stressors for (<b>a</b>) MDE, (<b>b</b>) GAD, and (<b>c</b>) K10 distress. Red squares represent the point estimates for the RERIs between sex and pandemic-related stressors; horizontal bars represent 95% CIs.</p>
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25 pages, 2595 KiB  
Review
The Role of Nanoparticles in Wine Science: Innovations and Applications
by Agnieszka Mierczynska-Vasilev
Nanomaterials 2025, 15(3), 175; https://doi.org/10.3390/nano15030175 - 23 Jan 2025
Viewed by 426
Abstract
Viticulture, the science of growing, cultivating, and harvesting grapes, and enology, the art and science of making wine, are rapidly evolving through innovative approaches aimed at improving the quality and efficiency of grape and wine production. This review explores the emerging use of [...] Read more.
Viticulture, the science of growing, cultivating, and harvesting grapes, and enology, the art and science of making wine, are rapidly evolving through innovative approaches aimed at improving the quality and efficiency of grape and wine production. This review explores the emerging use of nanoparticles, in particular gold, silver, and magnetic nanoparticles, to improve the quality, safety, and sustainability of both grape growing and winemaking processes. The unique properties of these nanoparticles, such as their small size, high surface area, and distinct chemical properties, enable them to address key challenges within the industry. In viticulture, nanoparticles have shown potential in protecting vines from pathogens, optimizing grape yield, and improving quality. In enology, nanoparticles are making a significant contribution to microbial control, reducing spoilage and refining wine analysis techniques, leading to improved product quality and safety. This review also highlights the synergy between different types of nanoparticles and their diverse applications, from microbial control in wine production to their use in innovative packaging solutions. In addition, nanoparticles have the potential to reduce dependence on agrochemicals and improve the sustainability of wine production, which is a promising avenue for future research. However, the integration of nanoparticles in viticulture and enology also poses regulatory and safety challenges, including the potential for nanoparticles to leach into wine products. Further research and regulatory advances are essential to ensure the safe and effective use of these technologies in winemaking. Overall, nanoparticles offer significant benefits to the wine industry, driving improvements in efficiency, sustainability, and quality. Full article
(This article belongs to the Section Environmental Nanoscience and Nanotechnology)
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<p>Schematic representation of the magnetic separation process for removing pathogenesis-related proteins from wine: (I) functionalization of MNPs using plasma deposition of acrylic acid; (II) capture of pathogenesis-related proteins, such as thaumatin-like proteins (TLPs) and chitinases (CHIs), from white wine, demonstrated using Verdejo wine as an example; (III) separation of pathogenesis-related proteins from wine by applying an external magnetic field. The photo with the red background illustrates <b>A</b> wine before treatment, <b>B</b> wine with dispersed MNPs, and <b>C</b> wine after the application of the magnetic field. Reprinted/adapted with permission from Ref. [<a href="#B69-nanomaterials-15-00175" class="html-bibr">69</a>]. Copyright 2017, Elsevier.</p>
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<p>Schematic illustration of the generation of smart surfaces and their use in wine. (<b>A</b>–<b>C</b>) show the physicochemical properties of the coatings. (<b>A</b>) FTIR spectrum of 2-methyl-2-oxazoline coating deposited on KBr. (<b>B</b>) High-resolution C1s XPS spectrum of 2-methyl-2-oxazoline coating. (<b>C</b>) XPS survey spectra showing the surface chemical composition of uncoated mesh surfaces, mesh surfaces coated with POx, and mesh surfaces coated with POx and modified with 68 nm gold nanoparticles. (<b>D</b>) SEM image of 68 nm gold nanoparticles immobilized on POx deposited on a mesh surface. Reprinted/adapted with permission from Ref. [<a href="#B74-nanomaterials-15-00175" class="html-bibr">74</a>]. Copyright 2023, Elsevier.</p>
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19 pages, 3798 KiB  
Article
Biochar and Nitrogen Fertilizer Promote Alfalfa Yield by Regulating Root Development, Osmoregulatory Substances and Improve Soil Physicochemical Properties
by Jinlong Chai, Hang Yang, Zhen Chen, Weifang Li, Dongqing Li and Xiaojun Yu
Agriculture 2025, 15(3), 239; https://doi.org/10.3390/agriculture15030239 - 23 Jan 2025
Viewed by 394
Abstract
In artificial grassland systems, the extensive use of inorganic nitrogen (N) fertilizers has greatly enhanced grassland yields but also caused significant environmental issues. The combined use of biochar and N fertilizer is recognized as an effective and sustainable approach to reducing environmental risks [...] Read more.
In artificial grassland systems, the extensive use of inorganic nitrogen (N) fertilizers has greatly enhanced grassland yields but also caused significant environmental issues. The combined use of biochar and N fertilizer is recognized as an effective and sustainable approach to reducing environmental risks while boosting crop production. However, the specific impacts of biochar and N on alfalfa yield, soil properties, and root morphology remain unclear. This study examined the effects of three biochar application rates (0, 10, 20 t hm−2) and four N application levels (0, 47, 94, 188 kg N hm−2 yr−1) on alfalfa growth and soil characteristics. Results revealed that biochar notably promoted root development and increased osmoregulatory substance content. It enhanced root biomass by improving root nodule count, root neck bud formation, and root neck diameter, while N application reduced root nodule numbers. Biochar and N application reduced soil bulk density by 0.8–10.5%, with biochar further increasing available phosphorus and potassium levels. Additionally, their combined use significantly elevated soil nitrate and ammonium concentrations. Overall, the synergy of biochar and nitrogen application enhances alfalfa yield by fostering better root growth and improving soil fertility. Full article
(This article belongs to the Section Agricultural Soils)
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<p>The effects of biochar application and varying N levels on leaf biomass (<b>a</b>–<b>c</b>), stem biomass (<b>d</b>–<b>f</b>), and total alfalfa yield (<b>g</b>–<b>i</b>) at different cutting in 2022. B0, B1, and B2 indicate biochar levels of 0, 10, and 20 t hm<sup>−2</sup>. N0, N1, N2, and N3 indicate nitrogen levels of 0, 47, 94, and 188 kg N hm<sup>−2</sup> yr<sup>−1</sup>. Different lowercase letters indicate significant differences among biochar treatments within the same nitrogen level, and different capital letters represent significant differences among nitrogen treatments. Error bars show the standard error of the mean. NS, *, and ** denote <span class="html-italic">p</span> &gt; 0.05, <span class="html-italic">p</span> &lt; 0.05, and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>The effects of biochar application and varying N levels on leaf biomass (<b>a</b>–<b>d</b>), stem biomass (<b>e</b>–<b>h</b>), and total alfalfa yield (<b>i</b>–<b>l</b>) at different cutting in 2023. B0, B1, and B2 indicate biochar levels of 0, 10, and 20 t hm<sup>−2</sup>. N0, N1, N2, and N3 indicate nitrogen levels of 0, 47, 94, and 188 kg N hm<sup>−2</sup> yr<sup>−1</sup>. Different lowercase letters indicate significant differences among biochar treatments within the same nitrogen level, and different capital letters represent significant differences among nitrogen treatments. Error bars show the standard error of the mean. NS, *, and ** denote <span class="html-italic">p</span> &gt; 0.05, <span class="html-italic">p</span> &lt; 0.05, and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>The effects of biochar application and varying N levels on leaf biomass (<b>a</b>–<b>d</b>), stem biomass (<b>e</b>–<b>h</b>), and total alfalfa yield (<b>i</b>–<b>l</b>) at different cutting in 2023. B0, B1, and B2 indicate biochar levels of 0, 10, and 20 t hm<sup>−2</sup>. N0, N1, N2, and N3 indicate nitrogen levels of 0, 47, 94, and 188 kg N hm<sup>−2</sup> yr<sup>−1</sup>. Different lowercase letters indicate significant differences among biochar treatments within the same nitrogen level, and different capital letters represent significant differences among nitrogen treatments. Error bars show the standard error of the mean. NS, *, and ** denote <span class="html-italic">p</span> &gt; 0.05, <span class="html-italic">p</span> &lt; 0.05, and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>The effects of biochar application and varying N levels on root length (<b>a</b>,<b>b</b>), root crown diameter (<b>c</b>,<b>d</b>), root crown bud number (<b>e</b>,<b>f</b>), nodule number (<b>g</b>,<b>h</b>), and root biomass (<b>i</b>,<b>j</b>) in 2022 and 2023. B0, B1, and B2 indicate biochar levels of 0, 10, and 20 t hm<sup>−2</sup>. N0, N1, N2, and N3 indicate nitrogen levels of 0, 47, 94, and 188 kg N hm<sup>−2</sup> yr<sup>−1</sup>. Different lowercase letters indicate significant differences among biochar treatments within the same nitrogen level, and different capital letters represent significant differences among nitrogen treatments. Error bars show the standard error of the mean. NS, *, and ** denote <span class="html-italic">p</span> &gt; 0.05, <span class="html-italic">p</span> &lt; 0.05, and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>The effects of biochar application and varying N levels on soluble sugar (<b>a</b>,<b>b</b>), soluble protein (<b>c</b>,<b>d</b>), proline (<b>e</b>,<b>f</b>), and malonaldehyde (<b>g</b>,<b>h</b>) in 2022 and 2023. B0, B1, and B2 indicate biochar levels of 0, 10, and 20 t hm<sup>−2</sup>. N0, N1, N2, and N3 indicate nitrogen levels of 0, 47, 94, and 188 kg N hm<sup>−2</sup> yr<sup>−1</sup>. Different lowercase letters indicate significant differences among biochar treatments within the same nitrogen level, and different capital letters represent significant differences among nitrogen treatments. Error bars show the standard error of the mean. NS, *, and ** denote <span class="html-italic">p</span> &gt; 0.05, <span class="html-italic">p</span> &lt; 0.05, and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>Effects of biochar and N levels on bulk density (<b>a</b>,<b>b</b>) and pH (<b>c</b>,<b>d</b>) in 2022 and 2023. B0, B1, and B2 indicate biochar levels of 0, 10, and 20 t hm<sup>−2</sup>. N0, N1, N2, and N3 indicate nitrogen levels of 0, 47, 94, and 188 kg N hm<sup>−2</sup> yr<sup>−1</sup>. Different lowercase letters indicate significant differences among biochar treatments within the same nitrogen level, and different capital letters represent significant differences among nitrogen treatments. Error bars show the standard error of the mean. NS, *, and ** denote <span class="html-italic">p</span> &gt; 0.05, <span class="html-italic">p</span> &lt; 0.05, and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>Effects of biochar and N level on NO<sub>3</sub><sup>−</sup>-N (<b>a</b>,<b>b</b>), NH<sub>4</sub><sup>+</sup>-N (<b>c</b>,<b>d</b>), available phosphorus (<b>e</b>,<b>f</b>), and available potassium (<b>g</b>,<b>h</b>) in 2022 and 2023. B0, B1, and B2 indicate biochar levels of 0, 10, and 20 t hm<sup>−2</sup>. N0, N1, N2, and N3 indicate nitrogen levels of 0, 47, 94, and 188 kg N hm<sup>−2</sup> yr<sup>−1</sup>. Different lowercase letters indicate significant differences among biochar treatments within the same nitrogen level, and different capital letters represent significant differences among nitrogen treatments. Error bars show the standard error of the mean. NS, *, and ** denote <span class="html-italic">p</span> &gt; 0.05, <span class="html-italic">p</span> &lt; 0.05, and <span class="html-italic">p</span> &lt; 0.01, respectively.</p>
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<p>Pearson correlation analysis among yields, alfalfa quality, root traits, root osmoregulatory substances, and soil physicochemical parameters. Leaf, leaf biomass; Stem, stem biomass; CP, crude protein; ADF, acid detergent fiber; NDF, neutral detergent fiber; RFV, relative feeding value; RL, root length; RCD, root crown diameter; RCBN, root crown bud number; NN, nodules number; RB, root biomass; SS, soluble sugar; SP, soluble protein; BD, bulk density; NO<sub>3</sub><sup>−</sup>N, nitric nitrogen; NH<sub>4</sub><sup>−</sup>N, ammonium nitrogen; AP, available phosphorus; AK, available potassium. *, ** and *** denote <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.001, respectively.</p>
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33 pages, 3916 KiB  
Article
Exploring Spatial–Temporal Coupling and Its Driving Factors of Cultural and Tourism Industry in the Beijing–Tianjin–Hebei Urban Agglomeration, China
by Huifang Du and Jianguo Liu
Sustainability 2025, 17(3), 890; https://doi.org/10.3390/su17030890 - 22 Jan 2025
Viewed by 481
Abstract
This study focuses on the 13 cities within the Beijing–Tianjin–Hebei (BTH) urban agglomeration, developing a sophisticated rating index system grounded in a factor–environment–effect framework to assess the coupling and coordinated development of the cultural and tourism industries across the region, alongside their spatiotemporal [...] Read more.
This study focuses on the 13 cities within the Beijing–Tianjin–Hebei (BTH) urban agglomeration, developing a sophisticated rating index system grounded in a factor–environment–effect framework to assess the coupling and coordinated development of the cultural and tourism industries across the region, alongside their spatiotemporal evolution dynamics. The study further delves into the internal constraints and external driving forces, aiming to identify the current state and key bottlenecks of regional cultural–tourism integration. The findings indicate that: (1) On the whole, the cultural and tourism industries in the region exhibit a fluctuating yet upward trajectory, with a robust coupling between the two systems. The coupling coordination has transitioned from the “uncoordinated state” to the “transition stage”. (2) Regionally, the degree of coupling coordination evolves from “uncoordinated” to “coordinated”. Cities have progressively advanced in their coupling coordination levels and shown certain spatial clustering characteristics. Based on the evolving types of coupling coordination, six distinct patterns are identified. Beijing and Tianjin have emerged as models of synchronized cultural–tourism development, while cities in Hebei are increasingly shifting toward a tourism-prioritized development model. (3) Cultural development effects represent the primary obstacle factors, while technological innovation, urban infrastructure, digital construction, and government investment emerge as the major driving forces. Specifically, the interactions between industrial structure and government investment, industrial structure and technological innovation, and urban environment and economic scale have a more significant impact on the development of the cultural–tourism coupling coordination development. Based on the preceding analysis, it is recommended to implement targeted policy measures to enhance collaboration between Beijing, Tianjin, and the surrounding cities in critical sectors. This should focus on expanding the synergies between culture and tourism, leveraging digital technologies to foster innovation and integration within the cultural and tourism industries. Such initiatives will help mitigate the regional disparities in the development of cultural–tourism integration and promote a more balanced and sustainable growth trajectory. Full article
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<p>The research framework.</p>
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<p>Trend of changes in the development evaluation index of cultural and tourism industries, and the mean values of coupling degree and coupling coordination degree.</p>
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<p>Trend of changes in the average levels of integration of development elements, development environment, and development effects.</p>
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<p>Evolution of coupling coordination degree ranking.</p>
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<p>Spatiotemporal distribution of the relative development index.</p>
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<p>Spatiotemporal distribution of the coupling coordination index.</p>
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10 pages, 715 KiB  
Article
Association of GSTM1 and GSTT1 Copy Number Variation with Chromosomal Aberrations in Nuclear Power Plant Workers Exposed to Occupational Ionizing Radiation
by Joong won Lee, Younghyun Lee and Yang Jee Kim
Toxics 2025, 13(2), 73; https://doi.org/10.3390/toxics13020073 - 22 Jan 2025
Viewed by 363
Abstract
Exposure to low-dose ionizing radiation in occupational settings raises concerns about chromosomal aberrations (CAs) and their potential impact on genomic stability. Copy number variations (CNVs), structural genomic changes, influence susceptibility to environmental stressors and radiation-induced damage. This study analyzed CAs in 180 nuclear [...] Read more.
Exposure to low-dose ionizing radiation in occupational settings raises concerns about chromosomal aberrations (CAs) and their potential impact on genomic stability. Copy number variations (CNVs), structural genomic changes, influence susceptibility to environmental stressors and radiation-induced damage. This study analyzed CAs in 180 nuclear power plant workers exposed to occupational radiation and 45 controls, stratified by GSTM1 and GSTT1 CNVs. Workers exhibited significantly higher frequencies of chromatid-type and chromosome-type aberrations, of 5.47 and 3.01 per 500 cells, respectively, compared to 3.57 and 0.64 in controls (p < 0.001 for both). In the relatively high-exposure group, chromatid-type aberrations decreased with increasing GSTM1 and GSTT1 copy numbers. For GSTM1, individuals with zero copies showed 6.37 ± 3.47 aberrations per 500 cells, compared to 5.02 ± 3.05 for one copy and 4.67 ± 2.40 for two or more copies (p = 0.06). A similar trend was observed for GSTT1, with 6.00 ± 3.29 aberrations per 500 cells for zero copies, 5.38 ± 2.79 for one copy, and 4.11 ± 4.26 for two or more copies (p = 0.05). Poisson regression analysis further supported these findings after adjusting for potential confounders such as age, smoking status, and alcohol intake. Workers with null genotypes exhibited a 1.36-fold increase in chromatid-type aberrations compared to those with higher copy numbers under relatively high-exposure conditions, suggesting a synergy effect between GSTM1 and GSTT1 null genotypes in modulating radiation-induced aberrations. These findings underscore the role of genetic susceptibility, particularly involving GSTM1 and GSTT1 CNVs, in modulating radiation-induced chromosomal damage. The observed gene–environment interaction in the relatively high-exposure group suggests that pre-existing CNVs contribute to chromosomal instability under radiation exposure. Full article
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<p>Frequency of chromatid-type and chromosome-type CAs according to GSTM1 and GSTT1 copy numbers in controls and relatively low- and high-exposure worker groups.</p>
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<p>Interaction between GST copy number and radiation exposure in the occurrence of chromatid-type aberrations; *, <span class="html-italic">p</span> &lt; 0.05 compared to the reference (for which the relative frequency ratio is 1.0).</p>
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22 pages, 5346 KiB  
Article
Antioxidant Synergy in a Mixture of Powder Plant Leaves and Effects on Metabolic Profile, Oxidative Status and Intestinal Morpho-Histochemical Features of Laying Hens
by Angela Gabriella D’Alessandro, Alessio Di Luca, Salvatore Desantis and Giovanni Martemucci
Animals 2025, 15(3), 308; https://doi.org/10.3390/ani15030308 - 22 Jan 2025
Viewed by 590
Abstract
Phenolic antioxidant intake is encouraged to prevent oxidative damage, and antioxidant synergy is considered an advantage in adding polyphenols from varied plants. This study investigated the antioxidant and synergistic interactions among olive leaf (OL), bay laurel (BL), and rosemary (RL) leaf powder mixture [...] Read more.
Phenolic antioxidant intake is encouraged to prevent oxidative damage, and antioxidant synergy is considered an advantage in adding polyphenols from varied plants. This study investigated the antioxidant and synergistic interactions among olive leaf (OL), bay laurel (BL), and rosemary (RL) leaf powder mixture (LPM: OL + BL + RL), using in vitro chemical tests [TPC, ORAC, TEAC-ABTS, FRAP; combination index (CI)], and in vivo validation on blood oxidative status, metabolic profile, and intestinal histomorphology in laying hens. The in vitro study indicated a whole higher antioxidant capacity for the LPM than respective single/double-leave combinations. The LPM CI value (IC50, 0.60) indicated a synergistic effect compared to the binary combinations. Thus, the LPM was validated in vivo through dietary supplementation on sixty Lohmann Brown hens (30 weeks old), reared in an indoor–outdoor rearing system divided. The hens were allocated into two experimental groups (n. 30): basal control diet group; and diet supplemented group with 6 g/kg feed of LPM) containing OL, BL, and RL (respectively, at 65.7%:18.9%:15.4%), for 60 days. The LPM improved (p < 0.05) the oxidative status (TAS, FRAP; ROMs, TBARs) and vitamin E level, metabolic and immunological profiles, and it induced region-specific changes in the morphology and carbohydrate composition of mucins along intestinal tracts of the animals. These findings could provide a valuable strategy for identifying synergistic combinations in functional feed formulations for laying hens. Full article
(This article belongs to the Section Poultry)
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<p>Effect of dietary leaf power mixture (LPM) supplementation on laying hens’ villus height and crypt depth. Data show the mean with error bars representing ± SD and Student’s <span class="html-italic">t</span>-test results. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Density of goblet cells (GCs) expressed as the number of cells per 100 µm of villus length in duodenum and ileum of control (Con) and leaf power mixture (LPM) supplemented laying hens stained with PAS and HID/AB 2.5 procedure to reveal both neutral and acidic mucins. Data show the mean with error bars representing ± SD and Student’s t-test results. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Representative pictures showing the different staining intensity with PAS (<b>A</b>,<b>B</b>) and HID/Alcian Blue pH 2.5 (AB2.5) (<b>C</b>,<b>D</b>) staining procedures in the duodenum and ileum villi of laying hens. (<b>A</b>,<b>B</b>) PAS-positive goblet cells exhibit magenta staining; the nuclei are stained with Mayer’s hemalum. (<b>C</b>,<b>D</b>) Goblet cells show HID positivity (brown) in the duodenum and both HID and AB 2.5 positivity (blue) in the ileum; the nuclei are stained with fast red. lp, lamina propria; asterisk, goblet cells. Scale bar: (<b>A</b>–<b>D</b>), 25 µm.</p>
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<p>Percentage of the intestinal goblet cells of Control and LPM hens producing neutral mucins (PAS, magenta), acidic sulphated glycans (HID, brown), and both non-sulphated and sulphated acidic glycans (HID/AB 2.5, green).</p>
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14 pages, 2035 KiB  
Article
Comprehensive Multi-Scale Optimisation of Rum Fermentation
by Tinashe W. Mangwanda, Joel B. Johnson, Ryan J. Batley, Steve Jackson, Tyryn McKeown and Mani Naiker
Beverages 2025, 11(1), 17; https://doi.org/10.3390/beverages11010017 - 22 Jan 2025
Viewed by 700
Abstract
This study applied response surface methodology (RSM) to optimise process parameters for rum fermentation. The primary aim was to enhance ethanol productivity through refined molasses conditioning and fermentation. Polyacrylamide flocculants were evaluated for molasses clarification, identifying an optimised blend which significantly outperformed individual [...] Read more.
This study applied response surface methodology (RSM) to optimise process parameters for rum fermentation. The primary aim was to enhance ethanol productivity through refined molasses conditioning and fermentation. Polyacrylamide flocculants were evaluated for molasses clarification, identifying an optimised blend which significantly outperformed individual flocculants. Statistical analyses revealed Flopam AN 956 SH as the top performer based on settling behaviour and mud qualities. Mixture modelling exposed optimised flocculant formulations that outperformed individual flocculants, indicating synergistic interactions. A central composite design (CCD) systematically evaluated the effects of temperature, oxygenation, and nutrient supplementation on yeast growth kinetics. It determined that 5 ppm O2, 32.19 °C, and 2.5% nutrients maximised the specific growth rate at 0.39 h−1 and ethanol yield at 9.84% v/v. The models characterised interactions, revealing nutrient–oxygen synergies that stimulated metabolism. Overall, fermentation efficiency and assurance for ethanol yield were increased through comprehensive multi-scale optimisation utilising factorial designs, validated analytics, and quantitative strain characterisation. Full article
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<p>(<b>a</b>) Predicted vs. actual graph and (<b>b</b>) contour plots for mud compaction during molasses clarification. Comparison between predicted and actual mud compaction values obtained from response surface methodology, showing the accuracy of the model in predicting compaction behaviour. The contour plot visualises the effects of flocculant dosages on compaction, providing insight into optimal conditions for molasses clarification.</p>
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<p>(<b>a</b>) Desirability and (<b>b</b>) response surface contour plots for mud compaction during molasses clarification. The plot highlights regions where optimal compaction is achieved, facilitating the selection of ideal flocculant formulations.</p>
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<p>Three-dimensional response surface contour plot for mud compaction during molasses clarification. The plot illustrates the combined effect of these variables, pinpointing the dosage levels that maximise compaction during molasses clarification.</p>
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<p>Profiler analysis of oxygenation, temperature, and nutrients interactions and the effect on μmax. The graphical profiler analysis shows the interaction effects of oxygenation, temperature, and nutrient supplementation on the maximum specific growth rate (μmax) of <span class="html-italic">Saccharomyces cerevisiae</span>. The profiler provides a detailed understanding of how these factors synergistically influence yeast metabolism.</p>
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<p>Fermentation isoresponse curve depicting ethanol yield under varied factorial conditions. This graph reveals the optimal fermentation conditions for maximizing ethanol yield, emphasising the combined effect of oxygenation at 5 ppm and temperature at 32.19 °C.</p>
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<p>Cube plot from Design Expert depicting interactive influences of oxygenation, temperature, and nutrients on the predicted μmax based on the quadratic response surface modelling. The quadratic response surface model depicted as a cube plot, illustrating the interactive influences of oxygenation, temperature, and nutrient supplementation on the maximum specific growth rate (μmax). The plot highlights critical regions where factor interactions lead to optimal growth kinetics and ethanol yield.</p>
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